This document analyzes the total factor productivity change of microfinance institutions in Ethiopia from 2004-2009 using a Malmquist productivity index approach. It finds that overall productivity increased by an average of 3.8% per year over the period. The main driver of productivity growth was a 10.1% average annual increase in technical efficiency, as most institutions improved their management practices. However, technological change declined by 5.8% on average, indicating worsening performance among the best-practice institutions. Overall, Ethiopian microfinance needs to pursue technological progress to better achieve its dual objectives of outreach to the poor and financial sustainability.

1. European Journal of Business and Management www.iiste.org
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Total Factor Productivity Change of Ethiopian Microfinance
Institutions (MFIs): A Malmquist Productivity Index Approach
(MPI)
Bereket Zerai Gebremichael
PhD Candidate, Department of Commerce and Management studies, Andhra University,
Visakhapatnam, Andhra Pradesh, India - 530 003 Email bereketzg@yahoo.com
D. Lalitha Rani
Professor of Marketing and Entrepreneurship, Department of Commerce and Management studies, Andhra
University, Visakhapatnam, Andhra Pradesh, India - 530 003 Email mlalitha09@yahoo.co.in
Abstract
By employing the Malmquist productivity index this study attempts to examine the total factor productivity
change in the Ethiopian micro finance institutions (MFIs) using a balanced panel dataset of 114
observations from 19 micro finance institutions over the period 2004-2009. The selection of inputs and
outputs for the study is based on the dual objectives of MFIs viz outreach and sustainability framework
which is in line with the prior study of (Gutierrez et al 2007, 2009). Consequently, we specify two inputs
and three outputs; the number of employees, and operating expenses are specified as inputs whereas the
outputs are interests and fee income, gross loan portfolio, and number of loans outstanding (number). The
result of the study indicated that over the period the malmquist productivity change experienced by the
micro finance industry as a whole has averaged 3.8 % annually. With the exception of the year 2004-2005
(slight decline in productivity, which was 0.2 percent) the micro finance industry has reported
productivity progress in the study period(i.e productivity rose of 5.5 percent, 5.8 percent,0.3 percent and 7.7
percent in the years 2005-2006, 2006-2007, 2007-2008 and 2008-2009 respectively. It is apparent from the
analysis that the main source of total factor productivity (TFP) growth for the MFIs was attributed to the
technical efficiency change(10.1 percent increase) as the result depicted that 16 out of 19 MFIs ( about
84 %) has shown improvement in technical efficiency changes. In contrast, only 5 out of 19 (26.3%)
MFIs have shown improvement in technological change but still the industry as a whole has exhibited a
decline in technological change (5.8 percent decrease over the period) and suggested that there has been a
deterioration in the performance of the best practicing micro finance institutions. Further the result showed
that pure technical efficiency increased by 8.9 percent while scale efficiency contributed on average 1.1
percent increase and hence suggested that during the study period the Ethiopian MFIs have experienced
mainly an increment of pure technical efficiency( improvement in management practices) rather than an
improvement in optimum size(scale efficiency change). Generally, an important implication for the
Ethiopian micro finance industry is that they need to pursue a technological progress in order to meet the
dual objectives of reaching many poor people and financial sustainability.
Key words: Productivity Change, Malmquist Productivity Index, Ethiopian MFIs,
1. Introduction
Microfinance Institutions (MFIs) are essential ingredients in the development processes of a country. MFIs
provide financial services to low-income households in developing countries around the world. Historically
microfinance institutions predominantly originated with a mission of a social objective, i.e., poverty
reduction. However, in the last two decades there has been a major shift in emphasis from the social
objective of poverty reduction towards the economic objective of sustainable and market based financial
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services (Rauf and Mahamood, 2009). Indeed, the mission of all microfinance institutions (MFIs) is to
provide banking services to the poor, that is, to lend very small sums to very poor borrowers(Mersland and
Strøm, 2009). To that end, it is undoubtedly true that the sustainability of micro-financing is very
important for these purposes. According to Hartarska (2005) micro finance institutions face unique
challenges because they must achieve a double bottom line: provide financial services to the poor (outreach)
and cover their costs (sustainability). Otero (1998) reveals that MFIs need to generate profit, but at the same
time, they are required to balance the social objectives of reaching low-income entrepreneurs with
generating a return for their investors.
In addition to this, in recent years, there has been an increase in internal and external pressures for the MFIs
to decrease dependency on subsidized or grant funding and to become financially sustainable (Bogan et al,
2007). However, serving the poor and being financially self sufficient seem contradictory. Closely looking
the challenges that MFIs are facing currently, there seem to be a need in dynamism that improve costs
effectiveness and productivity performances. More specifically, an efficient operation of the micro finance
industry might be a necessary condition for the well functioning of MFIs in the long run in meeting the dual
objectives (outreach to the poor and financial sustainability). Apparently, studies aiming at investigating
efficiency and productivity of these institutions have become appealing in an effort to improve their
outreach performances, remain competitive and becoming sustainable.
An important contribution in studies of performances of MFIs probably, in recent years, a few studies have
tried to investigate the efficiency of micro finance institutions (Nghiem 2004;Gutierrez-Nieto et al 2005,
2007; Abdul Qayyum and Munir Ahmed 2006; Hamiza Haq et al 2007; Bassem 2008; Hermes et al 2008;
Kabir Hassan and Benito Sanchez2009).However, at least to the best of our knowledge there are no analysis
that have been done specifically to investigate the productivity change of micro finance institutions.
Thus, this study aims to investigate the productivity change Ethiopian micro finance industry during the
period 2005-2009 by employing Malmquist index and is expected to show managers, practitioners and
policy makers the performance of Ethiopian microfinance institutions and thereby contribute to the absence
of literature in areas of MFIs.
The rest of the paper is organized as follows. In section 2, the paper puts brief background of the Ethiopian
micro finance industry. Section 3 provides data and methodology including input and output specifications.
Section 4 presents results and discussions. Finally, Section 5 ends up with conclusions.
2. Overview of Micro Finance Industry in Ethiopia
Considering the importance of microfinance to the development the country’s economic growth and
poverty reduction, the Ethiopian government has paved a way to establish micro finance institutions. The
formal microfinance industry began in Ethiopia in 1994/1995 with the government’s “the Licensing and
Supervision of Microfinance Institution Proclamation” designed to encourage Microfinance Institutions
(MFIs) to extend credit to both the rural and urban poor of the country.
As far as the financial products they provide is concerned, many of the Ethiopian MFIs offer similar
financial products and mainly use the group lending methodology, while individual lending is employed to
a limited extent (Amaha, 2008). Some of the loan products provided by MFIs include agricultural loans,
micro-business loans, micro and small enterprise loans (micro-bank loans), employee loans, package loans
(food security loans), and housing loans (Amha, 2008). At present, there are 30 MFIs working through 433
branch offices and 598 sub-branches and are serving over 2.3 million clients (AEMFI, 2010). They provide
financial service, mainly credit and saving and, in some cases, loan insurance. Almost all MFIs in the
country have poverty alleviation as an objective. They are, thus, meant to address the lower strata of micro-
entrepreneurs including those engaged in activities that are started and operated just for survival (Berihun et
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al, 2009). As of 2009 the industry has pulled a total asset of 7.2 billion Birr (55.1 million USD311), with 5.1
billion birr (38.9 million USD) in outstanding loans and mobilized a total deposit of 2.4 billion birr (18.4
Million USD) (AMFI, 2009). Indeed, the four largest MFIs namely, ACSI, DECSI, OCSSCO and Omo are
backed by the governments of the four most populous regional states and account for 81 per cent of client
outreach and 85 per cent of total loans outstanding(IFAD,2011). The two largest MFIs (also the two largest
in Africa), ACSI in Amhara and DECSI in Tigray, account for over half (55 per cent) of total outreach and
for almost two thirds (63 per cent) of loans outstanding (ibid). According (Pfister et al, 2008), Ethiopian
microfinance has made remarkable progress over the past decade, reaching almost two million clients in a
country of 77 million people. Nevertheless, financial services for the low-income population, poor farmers
and MSMEs are still characterized by limited outreach, high transaction costs for clients, a generally weak
institutional base, weak governance and a nominal ownership structure as well as dependence on
government and mother NGOs (ibid).
3. Data and Methodology
The study is mainly based on secondary data. It is based on the annual data covering the period from
2004-2009 for the 19 micro finance institutions operating in Ethiopia. In fact, there are 30 MFIs currently
operating in Ethiopia; however, data cannot be generated from all the MFIs as some lack sufficient data
while others are new to be included in the analysis. The data is extracted from the financial statements
provided by the Association of Ethiopian Microfinance Institutions (AEMFI), National Bank of Ethiopia
(NBE) and Mix market.
In financial institution literature there are three alternative methods which have been used for measuring the
productivity changes, namely Fisher index, Tornqvist index and the Malmquist Index (Sofian, 2007). Of
these, indeed, the Malmquist TFP index is the most widely used measure of productivity change (Casu et
al,). Grifell-Tatje and Lovell (1996) noted that the Malmquist index has three main advantages relative to
the Fischer and Tornqvist indices. Firstly, it does not require the profit maximization, or the cost
minimization, assumption. Secondly, it does not require information on the input and output prices. Finally,
if the researcher has panel data, it allows the decomposition of productivity changes into two components
(technical efficiency change or catching up, and technical change or changes in the best practice). Its main
disadvantage is the necessity to compute the distance functions. However, the Data Envelopment Analysis
(DEA) technique can be used to solve this problem
The Malmquist productivity index (MPI) evaluates the productivity change of decision making units (MFIs
studied) between two time periods. It can be defined as the product of Catch-up and Frontier-shift terms.
Catch-up or recovery is related to the degree in which a decision making unit (DMU) improves or worsens
efficiency frontier shift (or innovation) is a term which reflects the change in the efficiency its frontiers
between the two time periods (Cooper et al 2007).
As discussed above the Malmquist index measures productivity growth (change). An MFI’s productivity
change could be due to either change in technical efficiency or change in the technology – technological
progress in the industry– or both. The total factor productivity change is the product of technical efficiency
change and technological change. Technical efficiency change is decomposed into pure technical efficiency
and scale efficiency change.
The Malmquist index measures total factor productivity (TFP) change between two data points by
calculating the ratio of the distances of each data point relative to a common technology and it requires the
3
Commercial banks currency exchange rate of 13 ETB to 1 USD has been used
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inputs and outputs from one time period to be mixed with the technology of another time period. Following
Fare et al. (1994), this paper adopts the output-oriented Malmquist productivity change index, referring the
emphasis on the equi-proportionate increase of outputs, within the context of a given level of input. The
output-oriented Malmquist productivity change index can be expressed as follows:
Where the ratio outside the brackets is equal to the change of technical efficiency between time t and t+1,
representing the change in the relative distance of the observed production from the maximum potential
production; while the component inside the brackets is the geometric mean of the two productivity indexes,
representing the shift in production technologies (technical change) between time t and t+1. The product of
the two components (efficiency change and technical change) is the Malmquist productivity change (total
factor productivity change). In addition, technical efficiency change can be further decomposed into pure
technical efficiency change and scale efficiency change.
Therefore, the two terms in equation (1) are:;
The Malmquist productivity index can be interpreted as a measure of total factor productivity (TFP) growth.
Improvement in productivity, as well as improvement in efficiency and technology, is indicated by values
greater than one, whereas value less than one indicate regress.
Selection of inputs and outputs
In measuring the technical efficiency and productivity of financial institutions, the most serious problem
lies and in fact, remains a controversial issue in literature in defining outputs and inputs of such institutions
(see Berger and Humphrey (1997). Commonly there are three approaches to this problem, namely; the
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production approach, the intermediation approach, and the assets approach (Berger and Humphrey 1997,
Athanassoupoululo, 1997)
.
Under intermediation approach financial institutions are considered as institutions transferring resources
from savers to investors. In this approach, inputs are measured by the volume of loans and deposits
collected and funds borrowed from financial markets whereas outputs are the loans and investments. Under
production approach financial institutions are producers of deposits and loans. In this approach, the number
of accounts opened or transactions processed is the best measure output, while the number of employees,
physical capital and other operating costs used to perform those transactions are considered as inputs.
Finally under the assets approach it is assumed that the basic function of any financial institution is the
creation of credit (loan). And hence the value of assets of financial institutions acts as output in this
approach. Microfinance institutions are also financial institutions but their approach and motive differs
from other financial institutions. They target mainly poor persons often without any collateral requirements
and their motive is not only to maximize profit (Gutierrez-Neito et al. 2007).
The selection of inputs and outputs for this study is based on the dual objectives of micro finance
institutions viz outreach and sustainability framework which in line with the prior study of Gutierrez et
al (2007). consequently we specify two inputs and three outputs; the number of employees , and operating
expenses/administrative expenses are specified as the two inputs whereas the outputs are interests and fee
income, gross loan portfolio, and number of loans outstanding(number). Table 1 presents descriptive
statistics of the inputs and out puts.
4. Results and Discussion
Table 1 reports the descriptive statistics of the variables included in the analysis of productivity changes
including their mean, standard deviation, minimum and maximum values for the sample of 19 MFIs
during the period 2004-2009. It can be observed that the variables used in the study vary greatly among
the sample MFIs and suggested that the sample observation composed of large and small micro finances as
well, as measured in terms of gross loan portfolio and number of loan outstanding among others.
Changes in productivity over the period of study are summarized in table 2. It should be noted all that
values for total factor productivity (TFP) or any of its components that are greater than 1 indicate progress
in efficiency and values less than 1 regress. The Malmquist productivity change experienced by the micro
finance industry as a whole has averaged 3.8. % per year and suggest improvement in performance of MFIs
from 2004-2009. As the result showed over the sample period, the average annual rate of technical
efficiency change is 10.1% while the rate of technological change is -5.8%.
As indicated in (table 2) the analysis of the change in efficiencies (Malmquist indices) shows that
productivity has been increasing during the period 2004-2009. With the exception of the year 2004-2005
( slight decline in productivity, which is 0.2 percent) the MF industry has reported productivity
progress in the study period (productivity rose of 5.5 percent, 5.8 percent,0.3 percent and 7.7 percent in the
years 2005-2006, 2006-2007, 2007-2008 and 2008-2009 respectively).
By decomposing the Malmquist index, it is possible to determine the sources of productivity growth. As
explained previously, technical efficiency change (TEC) and technological change (TC) are the
efficiency changes (movement of micro finances towards the frontier -catching up) and technological
changes (frontier shift) respectively. In this regard, the sources of growth or decline in Ethiopian micro
finance industry are due to TEC, TC, or both. However, from table 2 and table 3, it is apparent that the
main source of TFP growth for the MFIs was attributed to the technical efficiency change(10.1 percent
increase) as the result depicted that 16 out of 19 MFIs (84 %) have shown improvement in TEC. On the
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contrary, only 5 out of 19 (26.3%) MFIs have shown improvement in TC but still the industry as a whole
has exhibited a decline in technological change (5.8 percent decrease over the period) and suggested that
there has been a deterioration in the performance of the best practicing micro finance institutions.
Furthermore, during the study period the improvement in productivity as the result of an average
efficiency increase of 10.1 percent was offset by the average technological decrease of 5.8 percent and
turn the industry to exhibit a 3.8 percent overall productivity gains. Furthermore, technical efficiency
change can be decomposed into its pure technical efficiency and scale efficiency. Accordingly, the result
showed that pure technical efficiency increased by 8.9 percent while scale efficiency contributed on
average 1.1 percent increase and hence suggested that during the study period the Ethiopian MFIs have
experienced mainly an increment of pure technical efficiency( improvement in management practices)
rather than in improvement in optimum size(scale efficiency change).
5. Conclusion
By employing the Malmquist productivity index this study attempts to examine the total factor productivity
change in the Ethiopian micro finance institutions (MFIs) using a balanced panel dataset of 114
observations from 19 micro finance institutions over the period of 2004-2009. The selection of inputs and
outputs for the study is based on the dual objectives of MFIs viz, outreach and sustainability framework.
Consequently, we specify two inputs and three outputs; the number of employees, and operating expenses
are specified as inputs whereas the outputs are interests and fee income, gross loan portfolio, and number of
loans outstanding (number).
The result of the study indicated that over the period the malmquist productivity change experienced by the
micro finance industry as a whole has averaged 3.8 % annually. From the analysis it is apparent that the
main source of TFP growth for the MFIs was attributed to the technical efficiency change. Though few
MFIs (5 out of 19 MFIs) have shown improvement in technological change, the industry as a whole has
exhibited a decline in technological change (5.8 percent decrease over the period) and suggested that there
has been deterioration in the performance of the best practicing micro finance institutions. By decomposing
the Malmquist index, it is possible to determine the sources of productivity growth. Accordingly, the result
showed that during the study period the Ethiopian MFIs have experienced mainly an increment of pure
technical efficiency (improvement in management practices) rather than an improvement in optimum size.
In sum, an important policy/strategic implication for the Ethiopian micro finance industry is that they need
to pursue a technological progress in order to meet the dual objectives of reaching many poor people and
financial sustainability.
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Table 1.Descriptive statistics of Variables (inputs and Outputs) in dollars
2004 2005 2006 2007 2008 2009
Output Gross loan Average 6328424.85 9744770.55 13766157.37 18844364.53 23339095.90 20927300.10
portfolio
Std dev 12797557.82 20163130.28 25564762.22 36565166.93 46163287.85 38405984.36
Max 46365572 77918547 85266397 118766535 155668558 131184763
Min 103480 170229 263382 280132 427230 786650
Number of Average 1088873.05 68101.30 81526.45 98452.95 108202.85 120616.70
loans
Std dev 3560694.682 130002.942 145643.921 170817.514 189609.428 197968.151
Max 15622650 434814 536804 597723 108202.85 120616.70
Min 1153 1365 1917 1924 2984 2800
Interest & Average 766112.75 1176197.55 1784483.84 1784483.84 3168793.15 3252691.95
fee income
Std dev 1531603.59 2394933.57 3333634.44 4602177.50 6408038.59 6325184.72
Max 5458600 8022074 11671356 16947735 25368310 25152802
Min 18806 32860 38236 74535 101127 152918
Input Operating Average 338073.07 456196.20 661512.40 839407.10 1116844.00 1121145.55
expenses
Std dev 483416.053 677295.702 890280.763 1188215.847 1799420.306 1522281.266
Max 1865700 2687450 3216371 4336629 7394112 5422833
Min 25894 34499 51585 63465 72290 132600
Number of Average 238.00 314.40 378.75 432.35 490.60 515.95
employees
Std dev 408.790 529.080 604.587 684.013 754.046 802.781
Max 1670 1915 2065 2363 2590 2732
Min 17 18 27 28 38 37
Table 2: Malmquist Index Summary of Annual Means
Year Technical Technological Pure Technical Scale Total Factor
efficiency change change (TC) Efficiency Change efficiency Productivity
(TEC) Change
(Malmquist)
2004-2005 1.139 0.876 1.195 0.953 0.998
2005-2006 1.193 0.885 1.140 1.047 1.055
2006-2007 1.147 0.923 1.042 1.101 1.058
2007-2008 0.967 1.037 0.989 0.978 1.003
2008-2009 1.075 1.002 1.092 0.984 1.077
Mean 1.101 0.942 1.089 1.011 1.038
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Table 3: Malmquist Index Summary of Ethiopian MFI’s Means
Micro finance Technical Technological Pure Technical Scale Efficiency Total Factor
Efficiency Change (TC) Efficiency Change Productivity
Change Change Change
(TEC) (Malmquist)
ACSI 1.045 0.983 1.000 1.045 1.027
ADCSI 1.002 0.974 1.000 1.002 0.976
AVFS 1.126 0.926 1.138 0.989 1.042
BGMFISC 1.283 0.921 1.260 1.018 1.182
Buusaa Gonofaa 1.159 0.961 1.116 1.039 1.114
DECSI 1.000 0.953 1.000 1.000 0.953
Eshet 0.959 1.064 0.986 0.973 1.020
Gasha 1.107 1.043 1.181 0.937 1.155
Metemamen 1.536 0.804 1.456 1.055 1.234
OCSSCO 1.091 0.929 1.094 0.997 1.014
OMO 1.249 0.840 1.244 1.004 1.049
PEACE 0.939 0.703 0.950 0.988 0.660
SFPI 1.052 0.986 1.031 1.021 1.037
Wasasa 1.127 0.959 1.069 1.055 1.080
Wisdom 0.995 0.988 0.988 1.006 0.983
Meklit 1.037 1.001 1.076 0.964 1.037
Sidama 1.286 0.835 1.269 1.013 1.074
SEYAMFI 1.175 1.049 1.000 1.175 1.232
Agar I 0.927 1.082 0.971 0.954 1.003
Mean 1.101 0.942 0.971 0.954 1.038
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